SoroushVahidi/scidocs huggingface dataset:
Distance Metric Learning with Application to Clustering with Side-Information
(2002)

Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many “plausible” ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are found. For these and other applications requiring good metrics, it is desirable that we provide a more systematic way for users to indicate what they consider “similar.” For instance, we may ask them to provide examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in , learns a distance metric over that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us to give efficient, local-optima-free algorithms. We also demonstrate empirically that the learned metrics can be used to significantly improve clustering performance.
Comparing Clusterings - An Overview
(2007)

As the amount of data we nowadays have to deal with becomes larger and larger, the methods that help us to detect structures in the data and to identify interesting subsets in the data become more and more important. One of these methods is clustering, i.e. segmenting a set of elements into subsets such that the elements in each subset are somehow ”similiar” to each other and elements of different subsets are ”unsimilar”. In the literature we can find a large variety of clustering algorithms, each having certain advantages but also certain drawbacks. Typical questions that arise in this context comprise:
Constrained K-means Clustering with Background Knowledge
(2001)

Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be profitably modified to make use of this information. In experiments with artificial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance.
Promoting Student Metacognition
(2012)

Imagine yourself as the instructor of an introductory undergraduate biology course. Two students from your course independently visit your office the week after the first exam. Both students are biology majors. Both regularly attend class and submit their assignments on time. Both appear to be eager, dedicated, and genuine students who want to learn biology. During each of their office hours visits, you ask them to share how they prepared for the first exam. Their stories are strikingly different (inspired by Ertmer and Newby, 1996). During office hours, Josephina expresses that she was happy the exam was on a Monday, because she had a lot of time to prepare the previous weekend. She shares that she started studying after work on Saturday evening and did not go out with friends that night. When queried, she also shares that she reread all of the assigned textbook material and made flashcards of the bold words in the text. She feels that she should have done well on the test, because she studied all Saturday night and all day on Sunday. She feels that she did everything she could do to prepare. That said, she is worried about what her grade will be, and she wants you to know that she studied really hard, so she should get a good grade on the exam. Later in the week, Maya visits your office. When asked how she prepared for the first exam, she explains that she has regularly reviewed the PowerPoint slides each evening after class since the beginning of the term 4 weeks ago. She also read the assigned textbook pages weekly, but expresses that she spent most of her time comparing the ideas in the PowerPoint slides with the information in the textbook to see how they were similar and different. She found several places in which things seemed not to agree, which confused her. She kept a running list of these confusions each week. When you ask what she did with these confusions, she shares that she
A Survey of Text Summarization Extractive Techniques
(2010)

Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. It is very difficult for human beings to manually summarize large documents of text. Text Summarization methods can be classified into extractive and abstractive summarization. An extractive summarization method consists of selecting important sentences, paragraphs etc. from the original document and concatenating them into shorter form. The importance of sentences is decided based on statistical and linguistic features of sentences. An abstractive summarization method consists of understanding the original text and re-telling it in fewer words. It uses linguistic methods to examine and interpret the text and then to find the new concepts and expressions to best describe it by generating a new shorter text that conveys the most important information from the original text document. In this paper, a Survey of Text Summarization Extractive techniques has been presented.
Multiple SVM-RFE for gene selection in cancer classification with expression data
(2005)

This paper proposes a new feature selection method that uses a backward elimination procedure similar to that implemented in support vector machine recursive feature elimination (SVM-RFE). Unlike the SVM-RFE method, at each step, the proposed approach computes the feature ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data. We tested the proposed method on four gene expression datasets for cancer classification. The results show that the proposed feature selection method selects better gene subsets than the original SVM-RFE and improves the classification accuracy. A Gene Ontology-based similarity assessment indicates that the selected subsets are functionally diverse, further validating our gene selection method. This investigation also suggests that, for gene expression-based cancer classification, average test error from multiple partitions of training and test sets can be recommended as a reference of performance quality.
Photogeometric Scene Flow for High-Detail Dynamic 3D Reconstruction
(2015)

Photometric stereo (PS) is an established technique for high-detail reconstruction of 3D geometry and appearance. To correct for surface integration errors, PS is often combined with multiview stereo (MVS). With dynamic objects, PS reconstruction also faces the problem of computing optical flow (OF) for image alignment under rapid changes in illumination. Current PS methods typically compute optical flow and MVS as independent stages, each one with its own limitations and errors introduced by early regularization. In contrast, scene flow methods estimate geometry and motion, but lack the fine detail from PS. This paper proposes photogeometric scene flow (PGSF) for high-quality dynamic 3D reconstruction. PGSF performs PS, OF, and MVS simultaneously. It is based on two key observations: (i) while image alignment improves PS, PS allows for surfaces to be relit to improve alignment, (ii) PS provides surface gradients that render the smoothness term in MVS unnecessary, leading to truly data-driven, continuous depth estimates. This synergy is demonstrated in the quality of the resulting RGB appearance, 3D geometry, and 3D motion.
Data Driven Ontology Evaluation
(2004)

The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.
Learning Across Scales - Multiscale Methods for Convolution Neural Networks
(2017)

In this work, we establish the relation between optimal control and training deep Convolution Neural Networks (CNNs). We show that the forward propagation in CNNs can be interpreted as a time-dependent nonlinear differential equation and learning can be seen as controlling the parameters of the differential equation such that the network approximates the data-label relation for given training data. Using this continuous interpretation, we derive two new methods to scale CNNs with respect to two different dimensions. The first class of multiscale methods connects low-resolution and high-resolution data using prolongation and restriction of CNN parameters inspired by algebraic multigrid techniques. We demonstrate that our method enables classifying highresolution images using CNNs trained with low-resolution images and vice versa and warm-starting the learning process. The second class of multiscale methods connects shallow and deep networks and leads to new training strategies that gradually increase the depths of the CNN while re-using parameters for initializations.
Toolpath Planning for Continuous Extrusion Additive Manufacturing
(2017)

Recent work in additive manufacturing has introduced a new class of 3D printers that operate by extruding slurries and viscous mixtures such as silicone, glass, epoxy, and concrete, but because of the fluid flow properties of these materials it is difficult to stop extrusion once the print has begun. Conventional toolpath generation for 3D printing is based on the assumption that the flow of material can be controlled precisely and the resulting path includes instructions to disable extrusion and move the print head to another portion of the model. A continuous extrusion printer cannot disable material flow, and so these toolpaths produce low quality prints with wasted material. This paper outlines a greedy algorithm for post-processing toolpath instructions that employs a Traveling Salesperson Problem (TSP) solver to reduce the distance traveled between subsequent space-filling curves and layers, which reduces unnecessary extrusion by at least 20% for simple object models on an open-source 3D printer.
Artificial Intelligence MArkup Language: A Brief Tutorial
(2013)

The purpose of this paper is to serve as a reference guide for the development of chatterbots implemented with the AIML language. In order to achieve this, the main concepts in Pattern Recognition area are described because the AIML uses such theoretical framework in their syntactic and semantic structures. After that, AIML language is described and each AIML command/tag is followed by an application example. Also, the usage of AIML embedded tags for the handling of sequence dialogue limitations between humans and machines is shown. Finally, computer systems that assist in the design of chatterbots with the AIML language are classified and described.
An Analysis of the Elastic Net Approach to the Traveling Salesman Problem
(1989)

This paper analyzes the elastic net approach (Durbin and Willshaw 1987) to the traveling salesman problem of finding the shortest path through a set of cities. The elastic net approach jointly minimizes the length of an arbitrary path in the plane and the distance between the path points and the cities. The tradeoff between these two requirements is controlled by a scale parameter K. A global minimum is found for large K, and is then tracked to a small value. In this paper, we show that (1) in the small K limit the elastic path passes arbitrarily close to all the cities, but that only one path point is attracted to each city, (2) in the large K limit the net lies at the center of the set of cities, and (3) at a critical value of K the energy function bifurcates. We also show that this method can be interpreted in terms of extremizing a probability distribution controlled by K. The minimum at a given K corresponds to the maximum a posteriori (MAP) Bayesian estimate of the tour under a natural statistical interpretation. The analysis presented in this paper gives us a better understanding of the behavior of the elastic net, allows us to better choose the parameters for the optimization, and suggests how to extend the underlying ideas to other domains.
A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation
(2016)

In the Object Recognition task, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose estimation using these approaches has received relatively less attention. In this work, we study how Convolutional Neural Networks (CNN) architectures can be adapted to the task of simultaneous object recognition and pose estimation. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations within CNNs represent object pose information and how this contradicts with object category representations. We extensively experiment on two recent large and challenging multi-view datasets and we achieve better than the state-of-the-art.
Optimizing esthetics for implant restorations in the anterior maxilla: anatomic and surgical considerations.
(2004)

The placement of dental implants in the anterior maxilla is a challenge for clinicians because of patients' exacting esthetic demands and difficult pre-existing anatomy. This article presents anatomic and surgical considerations for these demanding indications for implant therapy. First, potential causes of esthetic implant failures are reviewed, discussing anatomic factors such as horizontal or vertical bone deficiencies and iatrogenic factors such as improper implant selection or the malpositioning of dental implants for an esthetic implant restoration. Furthermore, aspects of preoperative analysis are described in various clinical situations, followed by recommendations for the surgical procedures in single-tooth gaps and in extended edentulous spaces with multiple missing teeth. An ideal implant position in all 3 dimensions is required. These mesiodistal, apicocoronal, and orofacial dimensions are well described, defining "comfort" and "danger" zones for proper implant position in the anterior maxilla. During surgery, the emphasis is on proper implant selection to avoid oversized implants, careful and low-trauma soft tissue handling, and implant placement in a proper position using either a periodontal probe or a prefabricated surgical guide. If missing, the facial bone wall is augmented using a proper surgical technique, such as guided bone regeneration with barrier membranes and appropriate bone grafts and/or bone substitutes. Finally, precise wound closure using a submerged or a semi-submerged healing modality is recommended. Following a healing period of between 6 and 12 weeks, a reopening procedure is recommended with a punch technique to initiate the restorative phase of therapy.
EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks.
(2007)

INTRODUCTION
The ability to continuously and unobtrusively monitor levels of task engagement and mental workload in an operational environment could be useful in identifying more accurate and efficient methods for humans to interact with technology. This information could also be used to optimize the design of safer, more efficient work environments that increase motivation and productivity.


METHODS
The present study explored the feasibility of monitoring electroencephalo-graphic (EEG) indices of engagement and workload acquired unobtrusively and quantified during performance of cognitive tests. EEG was acquired from 80 healthy participants with a wireless sensor headset (F3-F4,C3-C4,Cz-POz,F3-Cz,Fz-C3,Fz-POz) during tasks including: multi-level forward/backward-digit-span, grid-recall, trails, mental-addition, 20-min 3-Choice Vigilance, and image-learning and memory tests. EEG metrics for engagement and workload were calculated for each 1 -s of EEG.


RESULTS
Across participants, engagement but not workload decreased over the 20-min vigilance test. Engagement and workload were significantly increased during the encoding period of verbal and image-learning and memory tests when compared with the recognition/ recall period. Workload but not engagement increased linearly as level of difficulty increased in forward and backward-digit-span, grid-recall, and mental-addition tests. EEG measures correlated with both subjective and objective performance metrics.


DISCUSSION
These data in combination with previous studies suggest that EEG engagement reflects information-gathering, visual processing, and allocation of attention. EEG workload increases with increasing working memory load and during problem solving, integration of information, analytical reasoning, and may be more reflective of executive functions. Inspection of EEG on a second-by-second timescale revealed associations between workload and engagement levels when aligned with specific task events providing preliminary evidence that second-by-second classifications reflect parameters of task performance.
MuDeL: a language and a system for describing and generating mutants
(2002)

Mutation Testing is an approach for assessing the quality of a test case suite by analyzing its ability in distinguishing the product under test from a set of alternative products, the so-called mutants. The mutants are generated from the product under test by applying a set of mutant operators, which produce products with slight syntactical differences. The mutant operators are usually based on typical errors that occurs during the software development and can be related to a fault model. In this paper, we propose a language — named MuDeL — for describing mutant operators aiming not only at automating the mutant generation, but also at providing precision and formality to the operator descriptions. The language was designed using concepts that come from transformational and logical programming paradigms, as well as from context-free grammar theory. The language is illustrated with some simple examples. We also describe the mudelgen system, developed to support this language.
Direct-on-line-start permanent-magnet-assisted synchronous reluctance machine with ferrite magnets
(2017)

The possibility of applying low-cost ferrite magnets in a direct-on-line-start permanent-magnet-assisted synchronous reluctance machine (DOL PMaSynRM) for industrial applications, such as pump or ventilation systems was studied. The main target of this machine topology was to achieve a higher efficiency class compared to a counterpart induction machine (IM). Because of the weak properties of the ferrite magnets the motor had to be optimized for a low magnetic stress starting from the permanent magnets' point of view. The optimization procedure includes finding of a suitable rotor construction with a proper number of barriers, thickness of the barriers and thickness of the ferrite magnets. More detailed attention was dedicated to the analysis and elimination of irreversible demagnetization risk of ferrite magnets during start up period.
Internet use and the logics of personal empowerment in health.
(2008)

OBJECTIVES
The development of personal involvement and responsibility has become a strategic issue in health policy. The main goal of this study is to confirm the coexistence of three logics of personal empowerment through health information found on the Internet.


METHODS
A theoretical framework was applied to analyze personal empowerment from the user's perspective. A well-established Canadian Web site that offers information on personal health was used as a case study. A close-ended questionnaire was completed online by 2275 visitors and members of the Web site.


RESULTS
The findings confirm that the development of feelings of competence and control through Internet use is structured around three different logics. This implies three types of aptitudes that are fostered when the Internet is used to seek health information: doing what is prescribed (the professional logic), making choices based on personal judgment (the consumer logic), and mutual assistance (the community logic).


CONCLUSIONS
A recurring issue in three logics is the balance of roles and responsibilities required between the individual and the health provider.
Serendipity in the Research Literature: A Phenomenology of Serendipity Reporting
(2018)

The role of information sciences is to connect people with the information they need to accomplish the tasks that contribute to the greater societal good. While evidence of the wonderful contributions arising from serendipitous events abound, the framework describing the information behaviors exhibited during a serendipitous experience is just emerging and additional detail regarding the factors influencing those behaviors is needed in order to support these experiences effectively. Furthermore, it is important to understand the whole process of serendipity to fully appreciate the impact of research policies, disciplinary traditions and academic reporting practices on this unique type of information behavior. This study addresses those need by examining the phenomenon of serendipity as it is reported by biomedical and radiography researchers. A mixed method content analysis of existing research reports will be incorporated with semi-structured interviews of serendipity reporters to gain a robust understanding of the phenomenon of serendipity, and provide detail that may inform the design of information environments.
Zero-forcing beamforming with block diagonalization scheme for Coordinated Multi-Point transmission
(2012)

Coordinated Multi-Point (CoMP) transmission is a technology targeted for Long Term Evolution Advanced (LTE-A). The Joint Processing (JP) technique for CoMP can maximize system performance, which is achieved mainly with channel information-based beamforming algorithms. Precoding methods perform differently in various CoMP scenarios. In this paper, we propose a joint processing scheme in downlink CoMP transmission system. We apply block diagonal beamforming to downlink transmission, and assume perfect knowledge of downlink channels and transmit messages at each transmit point.
Tobler ' s First Law of Geography , Self Similarity , and Perlin Noise : A Large Scale Analysis of Gradient Distribution in Southern Utah with Application to Procedural Terrain Generation
(2014)

A statistical analysis finds that in a 160,000 square kilometer region of southern Utah gradients appear to be exponentially distributed at resolutions from 5m up to 1km. A simple modification to the Perlin noise generator changing the gradient distribution in each octave to an exponential distribution results in realistic and interesting procedurally generated terrain. The inverse transform sampling method is used in the amortized noise algorithm to achieve an exponential distribution in each octave, resulting in the generation of infinite non-repeating terrain with the same characteristics.